In this study, we mainly focus on AE-based models that are widely used to overcome the drawbacks of the existing linear recommender models, i.e., probabilistic matrix factorization (PMF) [1]}. As a pioneering work, AutoRec [2]} adopted the conventional AE, that directly takes the user or item rating vectors as input and reconstructs the rating vector at the output layer. Unlike AutoRec, collaborative filtering neural network (CFN) [3]} was built upon stacked DAE to make it more robust. Collaborative denoising autoencoder (CDAE) [4]} adopted DAE by adding latent user vectors. MultVAE [5]} utilized the multinomial likelihood on top of VAE. Furthermore, EASE\(^R\)  [6]} proposed the one-layer autoencoder and developed an analytic solution by relaxing the regularization of SLIM [7]}. Most recently, RecVAE [8]} improved on MultVAE by using a composite prior distribution for the latent codes under the \(\beta \) -VAE framework.
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